Daisuke Ikeda


The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics
Daisuke Ikeda | Yuta Taniguchi | Kazunori Koga
Proceedings of the 8th International Workshop on Mining Scientific Publications

Mainly due to the open access movement, the number of scholarly papers we can freely access is drastically increasing. A huge amount of papers is a promising resource for text mining and machine learning. Given a set of papers, for example, we can grasp past or current trends in a research community. Compared to the trend detection, it is more difficult to forecast trends in the near future, since the number of occurrences of some features, which are major cues for automatic detection, such as the word frequency, is quite small before such a trend will emerge. As a first step toward trend forecasting, this paper is devoted to finding subtle trends. To do this, the authors propose an index for keywords, called normalized impact index, and visualize keywords and their indices as a heat map. The authors have conducted case studies using some keywords already known as popular, and we found some keywords whose frequencies are not so large but whose indices are large.


Learning to Shift the Polarity of Words for Sentiment Classification
Daisuke Ikeda | Hiroya Takamura | Lev-Arie Ratinov | Manabu Okumura
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I